netlit Vignette

Redistricting Literature

Devin Judge-Lord, Adeline Lo & Kyler Hudson

library(knitr)

Using the netlit R Package

The netlit package provides functions to generate network statistics from a literature review. Specifically, netlit provides a wrapper for igraph functions to facilitate using network analysis in literature reviews.

Install this package with

devtools::install_github("judgelord/netlit")

To install netlit from CRAN, run the following:

install.packages("netlit")

Basic Usage

The review() function takes in a dataframe, data, that includes from and to columns (a directed graph structure).

In the example below, we use example data from this project on redistricting. These data are a set of related concepts (from and to) in the redistricting literature and citations for these relationships (cites and cites_empirical). See vignette("netlit") for more details on this example.

library(netlit)

data("literature")

literature %>% kable()
to from cites cites_empirical
detect gerrymandering computers Altman & McDonald 2010; Wang 2016; Altman & McDonald 2011; Ramachandran & Gold 2018 Wang 2016
public participation computers Altman & McDonald 2010; Altman & McDonald 2011 NA
preserve communities of interest number of competitive districts Gimpel & Harbridge-Yong 2020 Gimpel & Harbridge-Yong 2020
proportionality partisan advantage Caughey et al. 2017; Tamas 2019 NA
efficiency gap partisan gerrymandering Chen 2017 Chen 2017
constitutional test preserve communities of interest Stephanopoulos 2012 NA
detect gerrymandering mean-median vote comparison McDonald & Best 2015; Wang 2016; Wang 2016b Wang 2016b
constitutional test mean-median vote comparison McDonald & Best 2015, Best et al. 2018 NA
constitutional test partisan gerrymandering Kang 2017 NA
instability partisan gerrymandering Yoshinaka & Murhpy 2011 Yoshinaka & Murhpy 2011
elite polarization partisan gerrymandering Masket et al. 2012 Masket et al. 2012
number of minority representatives majority minority districts Atsusaka 2021 Atsusaka 2021
partisan advantage majority minority districts Cox & Holden 2011; Sabouni & Shelton 2021 Sabouni & Shelton 2021
voter turnout majority minority districts Fraga 2016 Fraga 2016
partisan advantage redistricting commission Cain 2011 NA
partisan donor advantage partisan gerrymandering Kirkland 2013 Kirkland 2013
legislator voting change in constituency boundaries Bertelli & Carson 2011, Hayes et al. 2010 Bertelli & Carson 2011, Hayes et al. 2010
legislative outcomes change in constituency boundaries Bertelli & Carson 2011, Gul & Pesendorfer 2010 Bertelli & Carson 2011
voter turnout competitiveness Moskowitz & Schneer 2019; Hunt 2018 Moskowitz & Schneer 2019; Hunt 2018
elite polarization sorting Krasa & Polborn 2018 Krasa & Polborn 2018
partisan advantage contiguity Chen & Rodden 2013 Chen & Rodden 2013
incumbent vote share electorate composition change Hood & McKee 2013; Ansolabehere & Snyder 2012 Hood & McKee 2013; Ansolabehere & Snyder 2012
personal vote electorate composition change Carsey et al. 2017, Bertelli & Carson 2011 Carsey et al. 2017
pork spending house-senate delegation alignment Chen 2010 NA
voter sense of place stability in voters’ fellow constituents Hayes & McKee 2011 Hayes & McKee 2011
voter information about their district change in constituency boundaries Winburn & Wagner 2010 NA
rolloff voter information about their district Winburn & Wagner 2010 Winburn & Wagner 2010
voter turnout voter information about their district Winburn & Wagner 2010 Winburn & Wagner 2010
split ticket voting voter information about their district Winburn & Wagner 2010 Winburn & Wagner 2010
campaign resource allocation electorate composition change Limbocker & You 2020 Limbocker & You 2020
partisan advantage geographic partisan distribution Chen & Rodden 2013; Duchin et al. 2019; Powell et al. 2020 Chen & Rodden 2013; Duchin et al. 2019; Powell et al. 2020
stability in voters’ fellow constituents preserve communities of interest Winburn & Wagner 2010 NA
voter information about their district preserve communities of interest Winburn & Wagner 2010 NA
rolloff preserve communities of interest Hayes & McKee 2011; Winburn & Wagner 2010 Hayes & McKee 2011
elite polarization number of competitive districts Grainger 2010 Grainger 2010
floor votes align with district preferences partisan advantage Caughey et al. 2017 NA
floor votes align with state preferences partisan advantage Caughey et al. 2017 NA
partisan advantage partisan advantage Arrington 2016; Campisi et al. 2019; Katz, King & Rosenblatt 2020 NA
legislator voting partisan advantage Lo 2013 Lo 2013
elite polarization partisan advantage Lo 2013 NA
efficiency gap partisan advantage Chen & Cottrell 2016 Chen & Cottrell 2016
number of competitive districts partisan advantage Goedert 2017; Goedert 2014 Goedert 2017, Yoshinaka & Murhpy 2011; Goedert 2014
house-senate delegation alignment proportionality Chen 2010 NA
minority representation compactness Webster 2013 NA
compactness compactness Barnes & Solomon 2020; Gatesman & Unwin 2021; Magleby & Mosesson 2018; De Assis et al. 2014; Altman & McDonald 2011; Liu et al. 2016, Chen & Rodden 2015, Tam Cho & Liu 2016, Saxon 2020 Barnes & Solomon 2020; Magleby & Mosesson 2018; De Assis et al. 2014; Chen & Rodden 2015, Tam Cho & Liu 2016, Saxon 2020
efficiency gap efficiency gap Stephanopoulos & McGhee 2015, McGhee 2014 McGhee 2014
equal population equal population Gatesman & Unwin 2021; Magleby & Mosesson 2018 Magleby & Mosesson 2018
majority representation redistricting commission Matsusaka 2010 Matsusaka 2010
elite ideological moderation redistricting commission McGhee & Shor 2017 McGhee & Shor 2017
competitiveness redistricting commission Carson et al. 2014, Grainger 2010, Masket et al. 2012; Goedert 2014 Carson et al. 2014, Grainger 2010, Masket et al. 2012; Goedert 2014
competitiveness redistricting by courts Carson et al. 2014 Carson et al. 2014
legislative majority-seeking behavior upcoming redistricting Makse 2014 Makse 2014
partisan dislocation partisan dislocation Deford, Eubank & Rodden 2020 NA
voter turnout compactness Ladewig 2018 Ladewig 2018
preserve communities of interest preserve communities of interest Makse 2012 Makse 2012
partisan advantage partisan gerrymandering Wang 2016; Cox & Holden 2011; Sabouni & Shelton 2021; Powell et al. 2020 Cox & Holden 2011; Sabouni & Shelton 2021; Powell et al. 2020
partisan gerrymandering preserve communities of interest Sabouni & Shelton 2021 Sabouni & Shelton 2021
candidate quality redistricting commission Williamson 2019 Williamson 2019
efficiency principle efficiency gap Veomett 2018 NA
efficiency gap wasted votes McGhee 2017 NA
ideological representation efficiency gap Caughey et al. 2017b Caughey et al. 2017b
support for redistricting process identification with governing party Fougere et al. 2010 Fougere et al. 2010
support for redistricting process redistricting commission Fougere et al. 2010 Fougere et al. 2010
proportionality efficiency gap Warrington 2018 NA
issue salience change in constituency boundaries Gardner 2012 NA
degree of political conflict change in constituency boundaries Gardner 2012 NA
majority representation partisan gerrymandering Goedert 2014; Nagle 2019 NA
inequality of opportunity vs outcome detect gerrymandering Wang et al. 2018 NA
electorate composition change change in constituency boundaries Bertelli & Carson 2011; Hood & McKee 2013; Ansolabehere & Snyder 2012 Bertelli & Carson 2011; Hood & McKee 2013; Ansolabehere & Snyder 2012

netlit offers four main functions: make_edgelist(), make_nodelist(), augment_nodelist(), and review().

review() is the primary function. The others are helper functions that perform the individual steps that review() does all at once. review() takes in a dataframe with at least two columns representing linked concepts (e.g., a cause and an effect) and returns data augmented with network statistics. Users must either specify “from” nodes and “to” nodes with the from and to arguments or include columns named from and to in the supplied data object.

review() returns a list of three objects:

  1. an augmented edgelist (a list of relationships with edge_betweenness calculated),
  2. an augmented nodelist (a list of concepts with degree and betweenness calculated), and
  3. a graph object suitable for use in other igraph functions or other network visualization packages.

Users may wish to include edge attributes (e.g., information about the relationship between the two concepts) or node attributes (information about each concept). We show how to do so below. But first, consider the basic use of review():

lit <- review(literature, from = "from", to = "to")

lit
## A netlit_review object with the following components:
## 
## $edgelist
##  - 69 edges
##  - edge attributes: edge_betweenness
## $nodelist
##  - 56 nodes
##  - node attributes: degree_in, degree_out, degree_total, betweenness
## $graph
##    an igraph object
edges <- lit$edgelist

edges %>%  kable()
from to edge_betweenness
computers detect gerrymandering 2.0
computers public participation 1.0
number of competitive districts preserve communities of interest 86.0
partisan advantage proportionality 19.5
partisan gerrymandering efficiency gap 13.5
preserve communities of interest constitutional test 7.0
mean-median vote comparison detect gerrymandering 2.0
mean-median vote comparison constitutional test 1.0
partisan gerrymandering constitutional test 1.0
partisan gerrymandering instability 8.0
partisan gerrymandering elite polarization 2.0
majority minority districts number of minority representatives 1.0
majority minority districts partisan advantage 23.0
majority minority districts voter turnout 1.0
redistricting commission partisan advantage 22.0
partisan gerrymandering partisan donor advantage 8.0
change in constituency boundaries legislator voting 1.0
change in constituency boundaries legislative outcomes 1.0
competitiveness voter turnout 3.0
sorting elite polarization 1.0
contiguity partisan advantage 24.0
electorate composition change incumbent vote share 2.0
electorate composition change personal vote 2.0
house-senate delegation alignment pork spending 12.0
stability in voters’ fellow constituents voter sense of place 9.0
change in constituency boundaries voter information about their district 4.0
voter information about their district rolloff 2.0
voter information about their district voter turnout 8.0
voter information about their district split ticket voting 10.0
electorate composition change campaign resource allocation 2.0
geographic partisan distribution partisan advantage 24.0
preserve communities of interest stability in voters’ fellow constituents 16.0
preserve communities of interest voter information about their district 22.0
preserve communities of interest rolloff 8.0
number of competitive districts elite polarization 1.0
partisan advantage floor votes align with district preferences 8.0
partisan advantage floor votes align with state preferences 8.0
partisan advantage partisan advantage 0.0
partisan advantage legislator voting 8.0
partisan advantage elite polarization 5.0
partisan advantage efficiency gap 15.0
partisan advantage number of competitive districts 71.0
proportionality house-senate delegation alignment 22.0
compactness minority representation 1.0
compactness compactness 0.0
efficiency gap efficiency gap 0.0
equal population equal population 0.0
redistricting commission majority representation 1.0
redistricting commission elite ideological moderation 1.0
redistricting commission competitiveness 2.0
redistricting by courts competitiveness 2.0
upcoming redistricting legislative majority-seeking behavior 1.0
partisan dislocation partisan dislocation 0.0
compactness voter turnout 1.0
preserve communities of interest preserve communities of interest 0.0
partisan gerrymandering partisan advantage 25.5
preserve communities of interest partisan gerrymandering 49.0
redistricting commission candidate quality 1.0
efficiency gap efficiency principle 10.0
wasted votes efficiency gap 6.0
efficiency gap ideological representation 10.0
identification with governing party support for redistricting process 1.0
redistricting commission support for redistricting process 1.0
efficiency gap proportionality 10.5
change in constituency boundaries issue salience 1.0
change in constituency boundaries degree of political conflict 1.0
partisan gerrymandering majority representation 7.0
detect gerrymandering inequality of opportunity vs outcome 3.0
change in constituency boundaries electorate composition change 4.0 
nodes <- lit$nodelist

nodes %>%  kable()
node degree_in degree_out degree_total betweenness
computers 0 2 2 0.0
number of competitive districts 1 2 3 64.0
partisan advantage 6 8 14 111.5
partisan gerrymandering 1 7 8 42.0
preserve communities of interest 2 6 8 79.0
mean-median vote comparison 0 2 2 0.0
majority minority districts 0 3 3 0.0
redistricting commission 0 6 6 0.0
change in constituency boundaries 0 6 6 0.0
competitiveness 2 1 3 2.0
sorting 0 1 1 0.0
contiguity 0 1 1 0.0
electorate composition change 1 3 4 3.0
house-senate delegation alignment 1 1 2 11.0
stability in voters’ fellow constituents 1 1 2 8.0
voter information about their district 2 3 5 17.0
geographic partisan distribution 0 1 1 0.0
proportionality 2 1 3 20.0
compactness 1 3 4 0.0
efficiency gap 4 4 8 25.5
equal population 1 1 2 0.0
redistricting by courts 0 1 1 0.0
upcoming redistricting 0 1 1 0.0
partisan dislocation 1 1 2 0.0
wasted votes 0 1 1 0.0
identification with governing party 0 1 1 0.0
detect gerrymandering 2 1 3 2.0
public participation 1 0 1 0.0
constitutional test 3 0 3 0.0
instability 1 0 1 0.0
elite polarization 4 0 4 0.0
number of minority representatives 1 0 1 0.0
voter turnout 4 0 4 0.0
partisan donor advantage 1 0 1 0.0
legislator voting 2 0 2 0.0
legislative outcomes 1 0 1 0.0
incumbent vote share 1 0 1 0.0
personal vote 1 0 1 0.0
pork spending 1 0 1 0.0
voter sense of place 1 0 1 0.0
rolloff 2 0 2 0.0
split ticket voting 1 0 1 0.0
campaign resource allocation 1 0 1 0.0
floor votes align with district preferences 1 0 1 0.0
floor votes align with state preferences 1 0 1 0.0
minority representation 1 0 1 0.0
majority representation 2 0 2 0.0
elite ideological moderation 1 0 1 0.0
legislative majority-seeking behavior 1 0 1 0.0
candidate quality 1 0 1 0.0
efficiency principle 1 0 1 0.0
ideological representation 1 0 1 0.0
support for redistricting process 2 0 2 0.0
issue salience 1 0 1 0.0
degree of political conflict 1 0 1 0.0
inequality of opportunity vs outcome 1 0 1 0.0

Edge and node attributes can be added using the edge_attributes and node_attributes arguments. edge_attributes is a vector that identifies columns in the supplied data frame that the user would like to retain. node_attributes is a separate dataframe that contains attributes for each node in the primary data set. The example node_attributes data include one column type indicating a type for each each node/variable/concept.

data("node_attributes")

node_attributes %>%  kable()
node type
campaign resource allocation effect
candidate quality effect
change in constituency boundaries condition
preserve communities of interest goal
competitiveness goal
computers condition
constitutional test metric
contiguity condition
sorting condition
detect gerrymandering goal
electorate composition change condition
elite ideological moderation effect
equal population metric
floor votes align with district preferences effect
house-senate delegation alignment effect
incumbent vote share effect
instability effect
legislative majority-seeking behavior effect
legislative outcomes effect
legislator voting effect
majority minority districts policy
mean-median vote comparison metric
minority representation effect
number of competitive districts goal
number of minority representatives effect
partisan advantage goal
partisan gerrymandering condition
personal vote effect
pork spending policy
proportionality goal
public participation goal
redistricting by courts policy
redistricting commission policy
rolloff effect
split ticket voting effect
stability in voters’ fellow constituents effect
upcoming redistricting condition
voter information about their district effect
voter sense of place effect
voter turnout effect
elite polarization effect
geographic partisan distribution condition
compactness value
efficiency gap metric
partisan dislocation metric
wasted votes metric
identification with governing party condition
partisan donor advantage effect
floor votes align with state preferences effect
majority representation effect
efficiency principle metric
ideological representation effect
support for redistricting process effect
issue salience effect
degree of political conflict effect
inequality of opportunity vs outcome effect 
lit <- review(literature,
              edge_attributes = c("cites", "cites_empirical"),
              node_attributes = node_attributes)

lit
## A netlit_review object with the following components:
## 
## $edgelist
##  - 69 edges
##  - edge attributes: cites, cites_empirical, edge_betweenness
## $nodelist
##  - 56 nodes
##  - node attributes: type, degree_in, degree_out, degree_total, betweenness
## $graph
##    an igraph object

Tip: to retain all variables from literature, use edge_attributes = names(literature).

More Advanced Uses: larger networks, visualizating your network, network descriptives

library(tidyverse)
library(magrittr)
library(ggraph)

We separated multiple cites to a theorized relationship with semicolons. Let’s count the total number of citations and the number of citations to empirical work by splitting out each cite and measuring the length of that vector.

# count cites 
literature %<>% 
  group_by(to, from) %>% 
  mutate(cite_weight = str_split(cites, ";")[[1]]  %>% length(),
         cite_weight_empirical = str_split(cites_empirical, ";",)[[1]] %>% length(),
         cite_weight_empirical = ifelse(is.na(cites_empirical), 0, cite_weight_empirical)) %>% 
  ungroup() 

# subsets 
literature %<>% mutate(communities_node = str_c(to, from) %>% str_detect("Commun"),
                       confound = case_when(
      from == "Preserve\nCommunities\nOf Interest" & to == "Rolloff" ~ T,
      from == "Voter\nInformation\nAbout Their\nDistrict" & to == "Rolloff" ~ T,
      from == "Preserve\nCommunities\nOf Interest" 
            & to == "Voter\nInformation\nAbout Their\nDistrict" ~ T,
      T ~ F),
              empirical = ifelse(!is.na(cites_empirical),
                                 "Empirical work", 
                                 "No empirical work"))

Now we use review() on this expanded edgelist, including all variables in the literature data with edge_attributes = names(literature).

# now with all node and edge attributes 
lit <- review(literature,
              edge_attributes = names(literature),
              node_attributes = node_attributes
              )

edges <- lit$edgelist

edges %>% kable()
from to cites cites_empirical cite_weight cite_weight_empirical communities_node confound empirical edge_betweenness
Computers Detect Gerrymandering Altman & McDonald 2010; Wang 2016; Altman & McDonald 2011; Ramachandran & Gold 2018 Wang 2016 4 1 FALSE FALSE Empirical work 2.0
Computers Public Participation Altman & McDonald 2010; Altman & McDonald 2011 NA 2 0 FALSE FALSE No empirical work 1.0
Number Of Competitive Districts Preserve Communities Of Interest Gimpel & Harbridge-Yong 2020 Gimpel & Harbridge-Yong 2020 1 1 TRUE FALSE Empirical work 86.0
Partisan Advantage Proportionality Caughey et al. 2017; Tamas 2019 NA 2 0 FALSE FALSE No empirical work 19.5
Partisan Gerrymandering Efficiency Gap Chen 2017 Chen 2017 1 1 FALSE FALSE Empirical work 13.5
Preserve Communities Of Interest Constitutional Test Stephanopoulos 2012 NA 1 0 TRUE FALSE No empirical work 7.0
Mean-Median Vote Comparison Detect Gerrymandering McDonald & Best 2015; Wang 2016; Wang 2016b Wang 2016b 3 1 FALSE FALSE Empirical work 2.0
Mean-Median Vote Comparison Constitutional Test McDonald & Best 2015, Best et al. 2018 NA 1 0 FALSE FALSE No empirical work 1.0
Partisan Gerrymandering Constitutional Test Kang 2017 NA 1 0 FALSE FALSE No empirical work 1.0
Partisan Gerrymandering Instability Yoshinaka & Murhpy 2011 Yoshinaka & Murhpy 2011 1 1 FALSE FALSE Empirical work 8.0
Partisan Gerrymandering Elite Polarization Masket et al. 2012 Masket et al. 2012 1 1 FALSE FALSE Empirical work 2.0
Majority Minority Districts Number Of Minority Representatives Atsusaka 2021 Atsusaka 2021 1 1 FALSE FALSE Empirical work 1.0
Majority Minority Districts Partisan Advantage Cox & Holden 2011; Sabouni & Shelton 2021 Sabouni & Shelton 2021 2 1 FALSE FALSE Empirical work 23.0
Majority Minority Districts Voter Turnout Fraga 2016 Fraga 2016 1 1 FALSE FALSE Empirical work 1.0
Redistricting Commission Partisan Advantage Cain 2011 NA 1 0 FALSE FALSE No empirical work 22.0
Partisan Gerrymandering Partisan Donor Advantage Kirkland 2013 Kirkland 2013 1 1 FALSE FALSE Empirical work 8.0
Change In Constituency Boundaries Legislator Voting Bertelli & Carson 2011, Hayes et al. 2010 Bertelli & Carson 2011, Hayes et al. 2010 1 1 FALSE FALSE Empirical work 1.0
Change In Constituency Boundaries Legislative Outcomes Bertelli & Carson 2011, Gul & Pesendorfer 2010 Bertelli & Carson 2011 1 1 FALSE FALSE Empirical work 1.0
Competitiveness Voter Turnout Moskowitz & Schneer 2019; Hunt 2018 Moskowitz & Schneer 2019; Hunt 2018 2 2 FALSE FALSE Empirical work 3.0
Sorting Elite Polarization Krasa & Polborn 2018 Krasa & Polborn 2018 1 1 FALSE FALSE Empirical work 1.0
Contiguity Partisan Advantage Chen & Rodden 2013 Chen & Rodden 2013 1 1 FALSE FALSE Empirical work 24.0
Electorate Composition Change Incumbent Vote Share Hood & McKee 2013; Ansolabehere & Snyder 2012 Hood & McKee 2013; Ansolabehere & Snyder 2012 2 2 FALSE FALSE Empirical work 2.0
Electorate Composition Change Personal Vote Carsey et al. 2017, Bertelli & Carson 2011 Carsey et al. 2017 1 1 FALSE FALSE Empirical work 2.0
House-Senate Delegation Alignment Pork Spending Chen 2010 NA 1 0 FALSE FALSE No empirical work 12.0
Stability In Voters’ Fellow Constituents Voter Sense Of Place Hayes & McKee 2011 Hayes & McKee 2011 1 1 FALSE FALSE Empirical work 9.0
Change In Constituency Boundaries Voter Information About Their District Winburn & Wagner 2010 NA 1 0 FALSE FALSE No empirical work 4.0
Voter Information About Their District Rolloff Winburn & Wagner 2010 Winburn & Wagner 2010 1 1 FALSE TRUE Empirical work 2.0
Voter Information About Their District Voter Turnout Winburn & Wagner 2010 Winburn & Wagner 2010 1 1 FALSE FALSE Empirical work 8.0
Voter Information About Their District Split Ticket Voting Winburn & Wagner 2010 Winburn & Wagner 2010 1 1 FALSE FALSE Empirical work 10.0
Electorate Composition Change Campaign Resource Allocation Limbocker & You 2020 Limbocker & You 2020 1 1 FALSE FALSE Empirical work 2.0
Geographic Partisan Distribution Partisan Advantage Chen & Rodden 2013; Duchin et al. 2019; Powell et al. 2020 Chen & Rodden 2013; Duchin et al. 2019; Powell et al. 2020 3 3 FALSE FALSE Empirical work 24.0
Preserve Communities Of Interest Stability In Voters’ Fellow Constituents Winburn & Wagner 2010 NA 1 0 TRUE FALSE No empirical work 16.0
Preserve Communities Of Interest Voter Information About Their District Winburn & Wagner 2010 NA 1 0 TRUE TRUE No empirical work 22.0
Preserve Communities Of Interest Rolloff Hayes & McKee 2011; Winburn & Wagner 2010 Hayes & McKee 2011 2 1 TRUE TRUE Empirical work 8.0
Number Of Competitive Districts Elite Polarization Grainger 2010 Grainger 2010 1 1 FALSE FALSE Empirical work 1.0
Partisan Advantage Floor Votes Align With District Preferences Caughey et al. 2017 NA 1 0 FALSE FALSE No empirical work 8.0
Partisan Advantage Floor Votes Align With State Preferences Caughey et al. 2017 NA 1 0 FALSE FALSE No empirical work 8.0
Partisan Advantage Partisan Advantage Arrington 2016; Campisi et al. 2019; Katz, King & Rosenblatt 2020 NA 3 0 FALSE FALSE No empirical work 0.0
Partisan Advantage Legislator Voting Lo 2013 Lo 2013 1 1 FALSE FALSE Empirical work 8.0
Partisan Advantage Elite Polarization Lo 2013 NA 1 0 FALSE FALSE No empirical work 5.0
Partisan Advantage Efficiency Gap Chen & Cottrell 2016 Chen & Cottrell 2016 1 1 FALSE FALSE Empirical work 15.0
Partisan Advantage Number Of Competitive Districts Goedert 2017; Goedert 2014 Goedert 2017, Yoshinaka & Murhpy 2011; Goedert 2014 2 2 FALSE FALSE Empirical work 71.0
Proportionality House-Senate Delegation Alignment Chen 2010 NA 1 0 FALSE FALSE No empirical work 22.0
Compactness Minority Representation Webster 2013 NA 1 0 FALSE FALSE No empirical work 1.0
Compactness Compactness Barnes & Solomon 2020; Gatesman & Unwin 2021; Magleby & Mosesson 2018; De Assis et al. 2014; Altman & McDonald 2011; Liu et al. 2016, Chen & Rodden 2015, Tam Cho & Liu 2016, Saxon 2020 Barnes & Solomon 2020; Magleby & Mosesson 2018; De Assis et al. 2014; Chen & Rodden 2015, Tam Cho & Liu 2016, Saxon 2020 6 4 FALSE FALSE Empirical work 0.0
Efficiency Gap Efficiency Gap Stephanopoulos & McGhee 2015, McGhee 2014 McGhee 2014 1 1 FALSE FALSE Empirical work 0.0
Equal Population Equal Population Gatesman & Unwin 2021; Magleby & Mosesson 2018 Magleby & Mosesson 2018 2 1 FALSE FALSE Empirical work 0.0
Redistricting Commission Majority Representation Matsusaka 2010 Matsusaka 2010 1 1 FALSE FALSE Empirical work 1.0
Redistricting Commission Elite Ideological Moderation McGhee & Shor 2017 McGhee & Shor 2017 1 1 FALSE FALSE Empirical work 1.0
Redistricting Commission Competitiveness Carson et al. 2014, Grainger 2010, Masket et al. 2012; Goedert 2014 Carson et al. 2014, Grainger 2010, Masket et al. 2012; Goedert 2014 2 2 FALSE FALSE Empirical work 2.0
Redistricting By Courts Competitiveness Carson et al. 2014 Carson et al. 2014 1 1 FALSE FALSE Empirical work 2.0
Upcoming Redistricting Legislative Majority-Seeking Behavior Makse 2014 Makse 2014 1 1 FALSE FALSE Empirical work 1.0
Partisan Dislocation Partisan Dislocation Deford, Eubank & Rodden 2020 NA 1 0 FALSE FALSE No empirical work 0.0
Compactness Voter Turnout Ladewig 2018 Ladewig 2018 1 1 FALSE FALSE Empirical work 1.0
Preserve Communities Of Interest Preserve Communities Of Interest Makse 2012 Makse 2012 1 1 TRUE FALSE Empirical work 0.0
Partisan Gerrymandering Partisan Advantage Wang 2016; Cox & Holden 2011; Sabouni & Shelton 2021; Powell et al. 2020 Cox & Holden 2011; Sabouni & Shelton 2021; Powell et al. 2020 4 3 FALSE FALSE Empirical work 25.5
Preserve Communities Of Interest Partisan Gerrymandering Sabouni & Shelton 2021 Sabouni & Shelton 2021 1 1 TRUE FALSE Empirical work 49.0
Redistricting Commission Candidate Quality Williamson 2019 Williamson 2019 1 1 FALSE FALSE Empirical work 1.0
Efficiency Gap Efficiency Principle Veomett 2018 NA 1 0 FALSE FALSE No empirical work 10.0
Wasted Votes Efficiency Gap McGhee 2017 NA 1 0 FALSE FALSE No empirical work 6.0
Efficiency Gap Ideological Representation Caughey et al. 2017b Caughey et al. 2017b 1 1 FALSE FALSE Empirical work 10.0
Identification With Governing Party Support For Redistricting Process Fougere et al. 2010 Fougere et al. 2010 1 1 FALSE FALSE Empirical work 1.0
Redistricting Commission Support For Redistricting Process Fougere et al. 2010 Fougere et al. 2010 1 1 FALSE FALSE Empirical work 1.0
Efficiency Gap Proportionality Warrington 2018 NA 1 0 FALSE FALSE No empirical work 10.5
Change In Constituency Boundaries Issue Salience Gardner 2012 NA 1 0 FALSE FALSE No empirical work 1.0
Change In Constituency Boundaries Degree Of Political Conflict Gardner 2012 NA 1 0 FALSE FALSE No empirical work 1.0
Partisan Gerrymandering Majority Representation Goedert 2014; Nagle 2019 NA 2 0 FALSE FALSE No empirical work 7.0
Detect Gerrymandering Inequality Of Opportunity Vs Outcome Wang et al. 2018 NA 1 0 FALSE FALSE No empirical work 3.0
Change In Constituency Boundaries Electorate Composition Change Bertelli & Carson 2011; Hood & McKee 2013; Ansolabehere & Snyder 2012 Bertelli & Carson 2011; Hood & McKee 2013; Ansolabehere & Snyder 2012 3 3 FALSE FALSE Empirical work 4.0 
nodes <- lit$nodelist

nodes %>%  kable()
node type degree_in degree_out degree_total betweenness
Computers condition 0 2 2 0.0
Number Of Competitive Districts goal 1 2 3 64.0
Partisan Advantage goal 6 8 14 111.5
Partisan Gerrymandering condition 1 7 8 42.0
Preserve Communities Of Interest goal 2 6 8 79.0
Mean-Median Vote Comparison metric 0 2 2 0.0
Majority Minority Districts policy 0 3 3 0.0
Redistricting Commission policy 0 6 6 0.0
Change In Constituency Boundaries condition 0 6 6 0.0
Competitiveness goal 2 1 3 2.0
Sorting condition 0 1 1 0.0
Contiguity condition 0 1 1 0.0
Electorate Composition Change condition 1 3 4 3.0
House-Senate Delegation Alignment effect 1 1 2 11.0
Stability In Voters’ Fellow Constituents effect 1 1 2 8.0
Voter Information About Their District effect 2 3 5 17.0
Geographic Partisan Distribution condition 0 1 1 0.0
Proportionality goal 2 1 3 20.0
Compactness value 1 3 4 0.0
Efficiency Gap metric 4 4 8 25.5
Equal Population metric 1 1 2 0.0
Redistricting By Courts policy 0 1 1 0.0
Upcoming Redistricting condition 0 1 1 0.0
Partisan Dislocation metric 1 1 2 0.0
Wasted Votes metric 0 1 1 0.0
Identification With Governing Party condition 0 1 1 0.0
Detect Gerrymandering goal 2 1 3 2.0
Public Participation goal 1 0 1 0.0
Constitutional Test metric 3 0 3 0.0
Instability effect 1 0 1 0.0
Elite Polarization effect 4 0 4 0.0
Number Of Minority Representatives effect 1 0 1 0.0
Voter Turnout effect 4 0 4 0.0
Partisan Donor Advantage effect 1 0 1 0.0
Legislator Voting effect 2 0 2 0.0
Legislative Outcomes effect 1 0 1 0.0
Incumbent Vote Share effect 1 0 1 0.0
Personal Vote effect 1 0 1 0.0
Pork Spending policy 1 0 1 0.0
Voter Sense Of Place effect 1 0 1 0.0
Rolloff effect 2 0 2 0.0
Split Ticket Voting effect 1 0 1 0.0
Campaign Resource Allocation effect 1 0 1 0.0
Floor Votes Align With District Preferences effect 1 0 1 0.0
Floor Votes Align With State Preferences effect 1 0 1 0.0
Minority Representation effect 1 0 1 0.0
Majority Representation effect 2 0 2 0.0
Elite Ideological Moderation effect 1 0 1 0.0
Legislative Majority-Seeking Behavior effect 1 0 1 0.0
Candidate Quality effect 1 0 1 0.0
Efficiency Principle metric 1 0 1 0.0
Ideological Representation effect 1 0 1 0.0
Support For Redistricting Process effect 2 0 2 0.0
Issue Salience effect 1 0 1 0.0
Degree Of Political Conflict effect 1 0 1 0.0
Inequality Of Opportunity Vs Outcome effect 1 0 1 0.0

The igraph object

# define igraph object as g
g <- lit$graph

g
## IGRAPH b737837 DN-B 56 69 -- 
## + attr: name (v/c), type (v/c), degree_in (v/n), degree_out (v/n),
## | degree_total (v/n), betweenness (v/n), cites (e/c), cites_empirical
## | (e/c), cite_weight (e/n), cite_weight_empirical (e/n),
## | communities_node (e/l), confound (e/l), empirical (e/c),
## | edge_betweenness (e/n)
## + edges from b737837 (vertex names):
## [1] Computers                        ->Detect\nGerrymandering            
## [2] Computers                        ->Public\nParticipation             
## [3] Number Of\nCompetitive\nDistricts->Preserve\nCommunities\nOf Interest
## [4] Partisan\nAdvantage              ->Proportionality                   
## + ... omitted several edges

What does it mean?

  • D means directed
  • N means named graph
  • W means weighted graph
  • name (v/c) means name is a node attribute and it’s a character
  • cite_weight (e/n) means cite_weight is an edge attribute and it’s numeric

With ggraph

We can also plot using the package ggraph package to plot the igraph object.

This package allows us to plot self-ties, but it is slightly more difficult to use ggplot features (e.g. colors and legend labels) compared to ggnetwork.

# best seed 1,4, *5*
set.seed(5)

p <- ggraph(g, layout = 'fr') + 
  geom_node_point(
    aes(color = degree_total %>% as.factor() ),
    size = 6, 
    alpha = .7
    ) + 
  geom_edge_arc2(
    aes(
      start_cap = circle(3, 'mm'),
      end_cap = circle(6, 'mm'),
      color = cite_weight %>% as_factor(),
      linetype = empirical
      ),
    curvature = 0,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "open")
    ) +
  geom_edge_loop(
      aes( color = cite_weight %>% as_factor(),
      start_cap = circle(5, 'mm'),
      end_cap = circle(2, 'mm'),
      linetype = empirical
      ),
      n = 300,
      strength = .6,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "open")
    ) +
  geom_node_text( aes(label = name), size = 2.3) + 
  ggplot2::theme_void() + 
  theme(legend.position="bottom") + 
  labs(edge_color = "Number of\nPublications",
       color = "Total Degree\nCentrality",
       edge_linetype = "") + 
scale_edge_color_viridis(discrete = TRUE,
                         option = "plasma", 
                         begin = 0, 
                         end = .9, 
                         direction = -1) +
  scale_color_viridis_d(option = "mako", 
                        begin = 1, 
                        end = .5)

p

Subgraphs

p + facet_wrap("communities_node")

p + facet_wrap("confound")

Betweenness

Edge Betweenness

ggraph(g, layout = 'fr') + 
  geom_node_point(size = 10, 
                  alpha = .1) + 
  theme_void() + 
  theme(legend.position="bottom"
        ) + 
  scale_color_viridis_c(begin = .5, 
                        end = 1, 
                        direction = -1, 
                        option = "cividis") + 
scale_edge_color_viridis(begin = 0.2, 
                         end = .9, 
                         direction = -1, 
                         option = "cividis")  +    
  geom_edge_arc2(
    aes(
    start_cap = circle(3, 'mm'),
    end_cap = circle(5, 'mm'),
    color = edge_betweenness,
    linetype = empirical
    ),
    curvature = .1,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "closed")) + 
    geom_edge_loop(aes(color = edge_betweenness))  +
  geom_node_text(aes(label = name), 
                 size = 2.3) + 
  labs(edge_color = "Edge Betweenness",
       color = "Node Betweenness",
       edge_linetype = "")

Node Betweenness

p <- ggraph(g, layout = 'fr') + 
  geom_node_point(
    aes(color = betweenness),
    size = 6, 
    alpha = .7
    ) + 
  geom_edge_arc2(
    aes(
      start_cap = circle(3, 'mm'),
      end_cap = circle(6, 'mm'),
      color = cite_weight %>% as_factor(),
      linetype = empirical
      ),
    curvature = 0,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "open")
    ) +
  geom_edge_loop(
      aes( color = cite_weight %>% as_factor(),
      start_cap = circle(5, 'mm'),
      end_cap = circle(2, 'mm'),
      linetype = empirical
      ),
      n = 300,
      strength = .6,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "open")
    ) +
  geom_node_text(aes(label = name), 
                  size = 2.3) + 
  theme_void() + 
  theme(legend.position="bottom") + 
  labs(edge_color = "Number of\nPublications",
       color = "Betweeneness",
       edge_linetype = "") + 
scale_edge_color_viridis(discrete = TRUE,
                         option = "plasma", 
                         begin = 0, 
                         end = .9, 
                         direction = -1) +
  scale_color_gradient2()

p 

ggraph(g, layout = 'fr') + 
  geom_node_point(aes(color = betweenness),
                  size = 10, 
                  alpha = 1) + 
  theme_void() + 
  theme(legend.position="bottom") + 
 scale_color_viridis_c(begin = .5, 
                       end = 1, 
                       direction = -1, 
                       option = "cividis") + 
scale_edge_color_viridis(begin = 0.2, 
                         end = .9, 
                         direction = -1, 
                         option = "cividis")  +    
  geom_edge_arc2(
    aes(
    start_cap = circle(3, 'mm'),
    end_cap = circle(5, 'mm'),
    color = edge_betweenness,
    linetype = empirical
    ),
    curvature = .1,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "closed")) + 
    geom_edge_loop(aes(color = edge_betweenness)) +
  labs(edge_color = "Edge Betweenness",
       color = "Node Betweenness",
       edge_linetype = "") + 
  geom_node_text(aes(label = name), 
                 size = 2.3)

Degree

ggraph(g, layout = 'fr') + 
  geom_node_point(aes(color = degree_total),
                  size = 10, 
                  alpha = 1) + 
  theme_void() + 
  theme(legend.position="bottom"        ) + 
  scale_color_gradient2() + 
scale_edge_color_viridis(begin = 0.2, 
                         end = .9, 
                         direction = -1, 
                         option = "cividis")  +    
  geom_edge_arc2(
    aes(
    start_cap = circle(3, 'mm'),
    end_cap = circle(5, 'mm'),
    color = edge_betweenness,
    linetype = empirical
    ),
    curvature = .1,
    arrow = arrow(length = unit(2, 'mm'), 
                  type = "closed")) + 
    geom_edge_loop(aes(color = edge_betweenness)) +
  labs(edge_color = "Edge Betweenness",
       color = "Total Degree",
       edge_linetype = "") + 
  geom_node_text(aes(label = name), 
                 size = 2.3)

About the example data

Articles were chosen according to specific selection criteria. We first identified articles published since 2010 that either 1) were published in one of eight high-ranking journals or 2) gained at least 50 citations according to Google Scholar. We then chose articles that contained four possible key terms in the title or abstract.

# Journal articles in example data
data("literature_metadata")

literature_metadata %>% kable()
ID Author Year Publication Title Citations Outside U.S.
Hayes & McKee 2011 Hayes & McKee 2011 AJPS The Intersection of Redistricting, Race, and Participation 41 NA
Katz, King & Rosenblatt 2020 Katz, King & Rosenblatt 2020 APSR Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies 20 NA
Lo 2013 Lo 2013 QJPS Legislative Responsiveness to Gerrymandering: Evidence from the 2003 Texas Redistricting 20 NA
Chen & Rodden 2013 Chen & Rodden 2013 QJPS Unintentional Gerrymandering: Political Geography and Electoral Bias in Legislatures 361 NA
Matsusaka 2010 Matsusaka 2010 QJPS Popular Control of Public Policy: A Quantitative Approach 138 NA
Moskowitz & Schneer 2019 Moskowitz & Schneer 2019 QJPS Reevaluating Competition and Turnout in US House Elections 10 NA
McGhee & Shor 2017 McGhee & Shor 2017 Perspectives on Politics Has the Top Two Primary Elected More Moderates? 20 NA
Wildgen & Engstrom 1980 Wildgen & Engstrom 1980 Legislative Studies Quarterly Spatial Distribution of Partisan Support and the Seats/Votes Relationship 30 NA
Cain 1985 Cain 1985 APSR Assessing the Partisan Effects of Redistricting 243 NA
Buchler 2005 Buchler 2005 Journal of Theoretical Politics Competition, Representation and Redistricting: The Case Against Competitive Congressional Districts 81 NA
Lublin & McDonald 2006 Lublin & McDonald 2006 Election Law Journal Is It Time to Draw the Line?: The Impact of Redistricting on Competition in State House Elections 44 NA
Caughey et al. 2017 Caughey et al.  2017 JOP Incremental Democracy: The Policy Effects of Partisan Control of State Government 92 NA
Glazer et al. 1987 Glazer et al.  1987 AJPS Partisan and Incumbency Effects of 1970s Congressional Redistricting 102 NA
Jacobson 2005 Jacobson 2005 Political Science Quarterly Polarized Politics and the 2004 Congressional and Presidential Elections 134 NA
Abramowitz et al. 2006 Abramowitz et al.  2006 JOP Incumbency, Redistricting, and the Decline of Competition in US House Elections 461 NA
Cain et al. 2005 Cain et al.  2005 Brookings Institution From Equality to Fairness: The Path of Political Reform since Baker v. Carr 56 NA
Griffin & Newman 2007 Griffin & Newman 2007 JOP The Unequal Representation of Latinos and Whites 145 NA
Grofman et al. 2000 Grofman et al.  2000 NCL Review Drawing Effective Minority Districts: A Conceptual Framework and Some Empirical Evidence 165 NA
McDonald 2006 McDonald 2006 PS: Political Science & Politics Drawing the Line on District Competition 76 NA
Desposato & Petrocik 2003 Desposato & Petrocik 2003 AJPS The Variable Incumbency Advantage: New Voters, Redistricting, and the Personal Vote 186 NA
Ashworth & Bueno de Mesquita 2006 Ashworth & Bueno de Mesquita 2006 JOP Delivering the Goods: Legislative Particularism in Different Electoral and Institutional Settings 201 NA
Hayes & McKee 2008 Hayes & McKee 2008 American Politics Research Toward a One-Party South? 65 NA
Winburn & Wagner 2010 Winburn & Wagner 2010 Political Research Quarterly Carving Voters Out: Redistricting’s Influence on Political Information, Turnout, and Voting Behavior 46 NA
Hayes & McKee 2009 Hayes & McKee 2009 AJPS The Participatory Effects of Redistricting 85 NA
Chen 2010 Chen 2010 AJPS The Effect of Electoral Geography on Pork Barreling in Bicameral Legislatures 38 NA
Cameron et al. 1996 Cameron et al.  1996 APSR Do Majority-Minority Districts Maximize Substantive Black Representation in Congress? 709 NA
Canon 1999 Canon 1999 Legislative Studies Quarterly Electoral Systems and the Representation of Minority Interests in Legislatures 77 NA
Gay 2007 Gay 2007 JOP Legislating Without Constraints: The Effect of Minority Districting on Legislators’ Responsiveness to Constituency Preferences 47 NA
Bratton & Haynie 1999 Bratton & Haynie 1999 JOP Agenda Setting and Legislative Success in State Legislatures: The Effects of Gender and Race 740 NA
Wyrick 1991 Wyrick 1991 American Politics Quarterly Management of Political Influence: Gerrymandering in the 1980s 10 NA
Barabas & Jerit 2004 Barabas & Jerit 2004 State Politics & Policy Quaterly Redistricting Principles and Racial Representation 44 NA
Shotts 2003 Shotts 2003 JOP Does Racial Redistricting Cause Conservative Policy Outcomes? Policy Preferences of Southern Representatives in the 1980s and 1990s 76 NA
Bullock 1995 Bullock 1995 American Politics Quarterly The Impact of Changing the Racial Composition of Congressional Districts on Legislators’ Roll Call Behavior 63 NA
Overby & Cosgrove 1996 Overby & Cosgrove 1996 JOP Unintended Consequences? Racial Redistricting and the Representation of Minority Interests 164 NA
Sharpe & Garand 2001 Sharpe & Garand 2001 Political Research Quarterly Race, Roll Calls, and Redistricting: The Impact of Race-Based Redistricting on Congressional Roll-Call 48 NA
LeVeaux & Garand 2003 LeVeaux & Garand 2003 Social Science Quarterly Race‐Based Redistricting, Core Constituencies, and Legislative Responsiveness to Constituency Change* 13 NA
Lyons & Galderisi 1995 Lyons & Galderisi 1995 Political Research Quarterly Incumbency, Reapportionment, and US House Redistricting 43 NA
Hirsch 2003 Hirsch 2003 Election Law Journal The United States House of Unrepresentatives: What Went Wrong in the Latest Round of Congressional Redistricting 159 NA
Grofman 1982 Grofman 1982 Political Geography Quarterly Reformers, Politicians, and the Courts: A Preliminary Look at US Redistricting in the 1980s 12 NA
Forgette & Winkle 2006 Forgette & Winkle 2006 Social Science Quarterly Partisan Gerrymandering and the Voting Rights Act 16 NA
Hetherington et al. 2003 Hetherington et al.  2003 JOP The Redistricting Cycle and Strategic Candidate Decisions in US House Races 91 NA
Carson et al. 2006 Carson et al.  2006 AJPS The Electoral Costs of Party Loyalty in Congress 342 NA
Lublin 1999 Lublin 1999 APSR Racial Redistricting and African-American Representation: A Critique of “Do Majority-Minority Districts Maximize Substantive Black Representation in Congress?” 216 NA
Forgette & Platt 2005 Forgette & Platt 2005 Political Geography Redistricting Principles and Incumbency Protection in the US Congress 33 NA
Carson & Crespin 2004 Carson & Crespin 2004 State Politics & Policy Quaterly The Effect of State Redistricting Methods on Electoral Competition in United States House of Representatives Races 100 NA
Katz et al. 2020 Katz et al.  2020 APSR Theoretical Foundations and Empirical Evaluations of Partisan Fairness in District-Based Democracies 20 NA
Bertelli & Carson 2011 Bertelli & Carson 2011 Electoral Studies Small Changes, Big Results: Legislative Voting Behavior in the Presence of New Voters 12 NA
Chen & Cottrell 2016 Chen & Cottrell 2016 Electoral Studies Evaluating Partisan Gains from Congressional Gerrymandering: Using Computer Simulations to Estimate the Effect of Gerrymandering in the U.S. House 62 NA
Hunt 2018 Hunt 2018 Electoral Studies When Does Redistricting Matter? Changing Conditions and Their Effects on Voter Turnout 8 NA
Sauger & Grofman 2016 Sauger & Grofman 2016 Electoral Studies Partisan Bias and Redistricting in France 15 1
Wong 2019 Wong 2019 BJPS Gerrymandering in Electoral Autocracies: Evidence from Hong Kong 16 1
Incerti 2018 Incerti 2018 Electoral Studies The Optimal Allocation of Campaign Funds in US House Elections 5 NA
Limbocker & You 2020 Limbocker & You 2020 Electoral Studies Campaign Styles: Persistency in Campaign Resource Allocation 2 NA
Carson et al. 2014 Carson et al.  2014 State Politics & Policy Quaterly Reevaluating the Effects of Redistricting on Electoral Competition, 1972–2012 38 NA
Makse 2014 Makse 2014 State Politics & Policy Quaterly The Redistricting Cycle, Partisan Tides, and Party Strategy in State Legislative Elections 12 NA
Hood & McKee 2013 Hood & McKee 2013 State Politics & Policy Quaterly Unwelcome Constituents: Redistricting and Countervailing Partisan Tides 7 NA
Goedert 2017 Goedert 2017 State Politics & Policy Quaterly The Pseudoparadox of Partisan Mapmaking and Congressional Competition 6 NA
Kirkland 2013 Kirkland 2013 State Politics & Policy Quaterly Wallet-Based Redistricting: Evidence for the Concentration of Wealth in Majority Party Districts 6 NA
Carsey et al. 2017 Carsey et al.  2017 State Politics & Policy Quaterly Rethinking the Normal Vote, the Personal Vote, and the Impact of Legislative Professionalism in U.S. State Legislative Elections 6 NA
Stephanopoulos & McGhee 2015 Stephanopoulos & McGhee 2015 University of Chicago Law Review Partisan Gerrymandering and the Efficiency Gap 345 NA
Chen & Rodden 2015 Chen & Rodden 2015 Election Law Journal Cutting Through the Thicket: Redistricting Simulations and the Detection of Partisan Gerrymanders 87 NA
Barnes & Solomon 2020 Barnes & Solomon 2020 Political Analysis Gerrymandering and Compactness: Implementation Flexibility and Abuse 13 NA
Atsusaka 2021 Atsusaka 2021 APSR A Logical Model for Predicting Minority Representation: Application to Redistricting and Voting Rights Cases 0 NA
Gatesman & Unwin 2021 Gatesman & Unwin 2021 Political Analysis Lattice Studies of Gerrymandering Strategies 0 NA
Magleby & Mosesson 2018 Magleby & Mosesson 2018 Political Analysis A New Approach for Developing Neutral Redistricting Plans 23 NA
Deford, Eubank & Rodden 2020 Deford, Eubank & Rodden 2020 Political Analysis Partisan Dislocation: A Precinct-Level Measure of Representation and Gerrymandering 0 NA
Krasa & Polborn 2018 Krasa & Polborn 2018 APSR Political Competition in Legislative Elections 41 NA
Saxon 2020 Saxon 2020 Political Analysis Reviving Legislative Avenues for Gerrymandering Reform with a Flexible, Automated Tool 3 NA
Kang 2017 Kang 2017 Michigan Law Review Gerrymandering and the Constitutional Norm Against Government Partisanship 61 NA
Stephanopoulos 2012 Stephanopoulos 2012 University of Pennsylvania Law Review Redistricting and the Territorial Community 61 NA
Altman & McDonald 2010 Altman & McDonald 2010 Duke Journal of Constitutional Law and Public Policy The Promise and Perils of Computers in Redistricting 87 NA
McDonald & Best 2015 McDonald & Best 2015 Election Law Journal Unfair Partisan Gerrymanders in Politics and Law: A Diagnostic Applied to Six Cases 73 NA
Tam Cho & Liu 2016 Tam Cho & Liu 2016 Election Law Journal Toward a Talismanic Redistricting Tool: A Computational Method for Identifying Extreme Redistricting Plans 76 NA
McGhee 2014 McGhee 2014 Legislative Studies Quarterly Measuring Partisan Bias in Single-Member District Electoral Systems 97 NA
Wang 2016 Wang 2016 Stanford Law Review Three Tests for Practical Evaluation of Partisan Gerrymandering 97 NA
Cox & Holden 2011 Cox & Holden 2011 University of Chicago Law Review Reconsidering Racial and Partisan Gerrymandering 76 NA
Stewart et al. 2019 Stewart et al.  2019 Nature Information Gerrymandering and Undemocratic Decisions 81 NA
Siegel-Hawley 2013 Siegel-Hawley 2013 Harvard Educational Review Educational Gerrymandering? Race and Attendance Boundaries in a Demographically Changing Suburb 58 NA
Richards 2014 Richards 2014 American Educational Research Journal The Gerrymandering of School Attendance Zones and the Segregation of Public Schools 91 NA
Fraga 2016 Fraga 2016 JOP Redistricting and the Causal Impact of Race on Voter Turnout 67 NA
De Assis et al. 2014 De Assis et al.  2014 Computers & Operations Research A Redistricting Problem Applied to Meter Reading in Power Distribution Networks 52 NA
Hayes et al. 2010 Hayes 2010 Legislative Studies Quarterly Redistricting, Responsiveness, and Issue Attention 52 NA
Liu et al. 2016 Liu et al.  2016 Swarm and Evolutionary Computation PEAR: A Massively Parallel Evolutionary Computation Approach for Political Redistricting Optimization and Analysis 62 NA
Yoshinaka & Murphy 2011 Yoshinaka & Murphy 2011 Political Research Quarterly The Paradox of Redistricting: How Partisan Mapmakers Foster Competition but Disrupt Representation 53 NA
Webster 2013 Webster 2013 Political Geography Reflections on Current Criteria to Evaluate Redistricting Plans 53 NA
Gentry et al. 2013 Gentry et al.  2013 American Journal of Transplantation Addressing Geographic Disparities in Liver Transplantation Through Redistricting 137 NA
Grainger 2010 Grainger 2010 The Journal of Law and Economics Redistricting and Polarization: Who Draws the Lines in California? 50 NA
Masket et al. 2012 Masket et al.  2012 PS: Political Science & Politics The Gerrymanderers are Coming! Legislative Redistricting Won’t Affect Competition or Polarization Much, No Matter Who Does It 57 NA
Altman & McDonald 2011 Altman & McDonald 2011 Journal of Statistical Software BARD: Better Automated Redistricting 84 NA
Gul & Pesendorfer 2010 Gul & Pesendorfer 2010 American Economic Review Strategic Redistricting 67 NA
Cain 2011 Cain 2011 Yale Law Journal Redistricting Commissions: A Better Political Buffer 128 NA
Arrington 2016 Arrington 2016 Election Law Journal A Practical Procedure for Detecting a Partisan Gerrymander 5 NA
Ladewig 2018 Ladewig 2018 Election Law Journal ‘‘Appearances Do Matter’’: Congressional District Compactness and Electoral Turnout 0 NA
Campisi et al. 2019 Campisi et al.  2019 Election Law Journal Declination as a Metric to Detect Partisan Gerrymandering 5 NA
Makse 2012 Makse 2012 Election Law Journal Defining Communities of Interest in Redistricting Through Initiative Voting 17 NA
Gimpel & Harbridge-Yong 2020 Gimpel & Harbridge-Yong 2020 Election Law Journal Conflicting Goals of Redistricting: Do Districts That Maximize Competition Reckon with Communities of Interest? 1 NA
Chen 2017 Chen 2017 Election Law Journal The Impact of Political Geography on Wisconsin Redistricting: An Analysis of Wisconsin’s Act 43 Assembly Districting Plan 22 NA
Ansolabehere & Snyder 2012 Ansolabehere & Snyder 2012 Election Law Journal The Effects of Redistricting on Incumbents 26 NA
Sabouni & Shelton 2021 Sabouni & Shelton 2021 Election Law Journal State Legislative Redistricting: The Effectiveness of Traditional Districting Principles in the 2010 Wave 0 NA
Williamson 2019 Williamson 2019 Election Law Journal Examining the Effects of Partisan Redistricting on Candidate Entry Decisions 2 NA
Veomett 2018 Veomett 2018 Election Law Journal Efficiency Gap, Voter Turnout, and the Efficiency Principle 23 NA
Tamas 2019 Tamas 2019 Election Law Journal American Disproportionality: A Historical Analysis of Partisan Bias in Elections to the U.S. House of Representatives 5 NA
Duchin et al. 2019 Duchin et al.  2019 Election Law Journal Locating the Representational Baseline: Republicans in Massachusetts 25 NA
McGhee 2017 McGhee 2017 Election Law Journal Measuring Efficiency in Redistricting 29 NA
Wang et al. 2018 Wang et al.  2018 Election Law Journal An Antidote for Gobbledygook: Organizing the Judge’s Partisan Gerrymandering Toolkit into Tests of Opportunity and Outcome 5 NA
Caughey et al. 2017b Caughey et al.  2017 Election Law Journal Partisan Gerrymandering and the Political Process: Effects on Roll-Call Voting and State Policies 31 NA
Powell et al. 2020 Powell et al.  2020 Election Law Journal Partisan Gerrymandering, Clustering, or Both? A New Approach to a Persistent Question 2 NA
Fougere et al. 2010 Fougere et al.  2010 Election Law Journal Partisanship, Public Opinion, and Redistricting 33 NA
Best et al. 2018 Best et al.  2018 Election Law Journal Considering the Prospects for Establishing a Packing Gerrymandering Standard 32 NA
Warrington 2018 Warrington 2018 Election Law Journal Quantifying Gerrymandering Using the Vote Distribution 34 NA
Gardner 2012 Gardner 2012 Election Law Journal How to Do Things with Boundaries: Redistricting and the Construction of Politics 14 NA
Goedert 2014 Goedert 2014 Election Law Journal Redistricting, Risk, and Representation: How Five State Gerrymanders Weathered the Tides of the 2000s 8 NA
Wang 2016b Wang 2016 Election Law Journal Three Practical Tests for Gerrymandering: Application to Maryland and Wisconsin 38 NA
Ramachandran & Gold 2018 Ramachandran & Gold 2018 Election Law Journal Using Outlier Analysis to Detect Partisan Gerrymanders: A Survey of Current Approaches and Future Directions 9 NA
Nagle 2019 Nagle 2019 Election Law Journal What Criteria Should Be Used for Redistricting Reform? 16 NA 
# count publications per journal
pub_table <- literature_metadata %>% 
  filter(str_detect(paste(literature$cites, collapse = "|"), Author)) %>% 
  count(Publication, name = "Articles") %>%
  mutate(Publication = case_when(
    Publication == "AJPS" ~ "American Journal of Political Science",
    Publication == "APSR" ~ "American Political Science Review",
    Publication == "BJPS" ~ "British Journal of Political Science",
    Publication == "JOP" ~ "The Journal of Politics",
    Publication == "NCL Review" ~ "North Carolina Law Review",
    Publication == "QJPS" ~ "Quarterly Journal of Political Science",
    TRUE ~ Publication
  )) 

pub_table %>% kable()
Publication Articles
American Journal of Political Science 4
American Economic Review 1
American Politics Research 1
American Political Science Review 4
Computers & Operations Research 1
Duke Journal of Constitutional Law and Public Policy 1
Election Law Journal 27
Electoral Studies 4
The Journal of Politics 2
Journal of Statistical Software 1
Legislative Studies Quarterly 2
Michigan Law Review 1
Perspectives on Politics 1
Political Analysis 5
Political Geography 1
Political Research Quarterly 1
PS: Political Science & Politics 2
Quarterly Journal of Political Science 4
Stanford Law Review 1
State Politics & Policy Quaterly 6
Swarm and Evolutionary Computation 1
The Journal of Law and Economics 1
University of Chicago Law Review 2
University of Pennsylvania Law Review 1
Yale Law Journal 1 
# save list of journals as .tex table to include in a paper
pub_table %>% 
  kable() %>% 
  save_kable(file = "tables/pub_table.tex")

data("literature")

# save example concepts as .tex table to include in a paper
literature %>%
  select(-cites_empirical) %>% 
  head() %>% 
  kable() %>% 
  save_kable(file = "tables/literature_example.tex")